Monocular 3D Human Pose Estimation Using Transfer Learning and Improved CNN Supervision
Year: 2016
Type: preprint
Abstract: We propose a new CNN-based method for regressing 3D human body pose from asingle image that improves over the state-of-the-art on standard benchmarks by
more than 25%. Our approach addresses the limited generalizability of models
trained solely on the starkly limited publicly available 3D body pose data.
Improved CNN supervision leverages first and second order parent relationships
along the skeletal kinematic tree, and improved multi-level skip connections to
learn better representations through implicit modification of the loss
landscape. Further, transfer learning from 2D human pose prediction
significantly improves accuracy and generalizability to unseen poses and camera
views. Additionally, we contribute a new benchmark and training set for human
body pose estimation from monocular images of real humans, that has ground
truth captured with marker-less motion capture. It complements existing corpora
with greater diversity in pose, human appearance, clothing, occlusion, and
viewpoints, and enables increased scope of augmentation. The benchmark covers
outdoors and indoor scenes.
Source: arXiv (Cornell University)
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2558486663